ALE_final_output
Kenzie Cooke
2025-07-01
1 Bottle sample data curation
1.1 Import/filter data
1.2 Filtered chem datasets
1.3 Coral TA stats
Generate mean, sd, sem TA for each coral
1.4 Growth dataframes
Import ta stats from above filtered data sets into growth dataframes. Create four based on different filtering of chem data.
1.5 DECIDE CUTOFF for PCO2 values
Choose which data sets to use for all figures and tables. Carb chem data excludes bottle samples with deltas >5, with precipitaiton, and with Seacarb generated pco2 values greater than the cutoff chosen above. We have chosen 2000
2 Seacarb output
Carb chem parameters generated using seacarb with measured DIC and TA values.
2.1 Coral beakers
Generate seacarb parameters for beakers with corals by treatment
2.2 Blank Beakers
Generate seacarb parameters for blank beakers by treatment
2.3 Seacarb stats by treatment
Create stats with mean, sd, and SEM for all parameters grouped by treatment (corals and empty beakers separate)
2.4 Create Seacarb Table
Export stats to carb chem table for manuscript
## # A tibble: 34 × 6
## Type Variable AMB_Value ELEV_Value HI_Value XHI_Value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Regular n 68 61 44 33
## 2 Blank n 14 11 11 7
## 3 Regular ALK_mean 0.00246 0.00367 0.00450 0.00460
## 4 Blank ALK_mean 0.00254 0.00374 0.00454 0.00461
## 5 Regular ALK_SEM 0.0000116 0.0000373 0.0000743 0.000111
## 6 Blank ALK_SEM 0.0000271 0.000102 0.0000926 0.000150
## 7 Regular DIC_mean 0.00212 0.00298 0.00358 0.00368
## 8 Blank DIC_mean 0.00219 0.00305 0.00358 0.00374
## 9 Regular DIC_SEM 0.0000114 0.0000281 0.0000563 0.0000832
## 10 Blank DIC_SEM 0.0000229 0.0000813 0.0000724 0.000142
## # ℹ 24 more rows3 Statistics
3.1 Treatment~TA ANOVA
Was alkalinity statistically different between treatments?
## Df Sum Sq Mean Sq F value Pr(>F)
## treatment 3 26.047 8.682 253 <2e-16 ***
## Residuals 36 1.236 0.034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = avg_ta_mmol ~ treatment, data = grdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47221 -0.07406 -0.00581 0.12531 0.29886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.44920 0.05858 41.81 < 2e-16 ***
## treatmentELEV 1.15477 0.08285 13.94 4.46e-16 ***
## treatmentHI 1.90932 0.08285 23.05 < 2e-16 ***
## treatmentXHI 2.02485 0.08285 24.44 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1853 on 36 degrees of freedom
## Multiple R-squared: 0.9547, Adjusted R-squared: 0.9509
## F-statistic: 253 on 3 and 36 DF, p-value: < 2.2e-16## treatment emmean SE df lower.CL upper.CL .group
## AMB 2.45 0.0586 36 2.33 2.57 a
## ELEV 3.60 0.0586 36 3.49 3.72 b
## HI 4.36 0.0586 36 4.24 4.48 c
## XHI 4.47 0.0586 36 4.36 4.59 c
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 4 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.## contrast estimate SE df t.ratio p.value
## AMB - ELEV -1.155 0.0829 36 -13.938 <.0001
## AMB - HI -1.909 0.0829 36 -23.045 <.0001
## AMB - XHI -2.025 0.0829 36 -24.440 <.0001
## ELEV - HI -0.755 0.0829 36 -9.107 <.0001
## ELEV - XHI -0.870 0.0829 36 -10.502 <.0001
## HI - XHI -0.116 0.0829 36 -1.394 0.5109
##
## P value adjustment: tukey method for comparing a family of 4 estimatesTreatment summaries
## # A tibble: 4 × 3
## Treatment mean_ta sem_ta
## <fct> <dbl> <dbl>
## 1 AMB 2467. 11.7
## 2 ELEV 3625. 32.0
## 3 HI 4355. 67.6
## 4 XHI 4530. 68.43.2 Calcification
3.2.1 Linear models and ANOVA
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: gr_t1t3 ~ avg_ta_mmol + (1 | geno) + (1 | tank)
## Data: grdata
##
## REML criterion at convergence: -15.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.61467 -0.84337 0.04106 0.74427 2.14702
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.001191 0.03451
## geno (Intercept) 0.004232 0.06505
## Residual 0.029966 0.17311
## Number of obs: 40, groups: tank, 4; geno, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06599 0.13419 33.35580 -0.492 0.626
## avg_ta_mmol 0.19383 0.03334 33.71623 5.813 1.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## avg_ta_mmol -0.928## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## avg_ta_mmol 1.0127 1.0127 1 33.716 33.795 1.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## gr_t1t3 ~ avg_ta_mmol + (1 | geno) + (1 | tank)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 7.7054 -5.4108
## (1 | geno) 4 7.1532 -6.3065 1.10433 1 0.2933
## (1 | tank) 4 7.6242 -7.2484 0.16239 1 0.6870## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 7.7054 -5.4108
## (1 | tank) 1 4 7.6242 -7.2484 0.16239 1 0.6870
## (1 | geno) 2 3 7.1071 -8.2142 1.03416 1 0.3092
##
## Backward reduced fixed-effect table:
## Eliminated Df Sum of Sq RSS AIC F value Pr(>F)
## avg_ta_mmol 0 1 0.99993 2.2732 -112.71 29.843 3.104e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## gr_t1t3 ~ avg_ta_mmolno genotype effect Simplest linear model:
##
## Call:
## lm(formula = gr_t1t3 ~ avg_ta_mmol, data = grdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30454 -0.15986 0.00683 0.13630 0.44281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05072 0.13359 -0.380 0.706
## avg_ta_mmol 0.19144 0.03504 5.463 3.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.183 on 38 degrees of freedom
## Multiple R-squared: 0.4399, Adjusted R-squared: 0.4251
## F-statistic: 29.84 on 1 and 38 DF, p-value: 3.104e-06## Analysis of Variance Table
##
## Response: gr_t1t3
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.99993 0.99993 29.843 3.104e-06 ***
## Residuals 38 1.27324 0.03351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "R-Squared: 0.439881998425931"## [1] "slope: 0.191444670704968"## [1] "p = 3.10401201218377e-06"## [1] "intercept: -0.050716687770756"3.3 Linear Extension
3.3.1 Linear models and ANOVA
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: le_t1t3e ~ avg_ta_mmol + (1 | geno) + (1 | tank)
## Data: grdata
##
## REML criterion at convergence: -139.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.53959 -0.51073 -0.00919 0.61262 2.06220
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.000e+00 0.000000
## geno (Intercept) 5.267e-05 0.007258
## Residual 1.216e-03 0.034867
## Number of obs: 40, groups: tank, 4; geno, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.007948 0.025904 37.126089 0.307 0.7607
## avg_ta_mmol 0.013115 0.006690 36.763581 1.961 0.0575 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## avg_ta_mmol -0.963
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## avg_ta_mmol 0.0046729 0.0046729 1 36.764 3.8437 0.05755 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## le_t1t3e ~ avg_ta_mmol + (1 | geno) + (1 | tank)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 69.678 -129.36
## (1 | geno) 4 69.544 -131.09 0.26857 1 0.6043
## (1 | tank) 4 69.678 -131.36 0.00000 1 1.0000## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 69.678 -129.36
## (1 | tank) 1 4 69.678 -131.36 0.00000 1 1.0000
## (1 | geno) 2 3 69.544 -133.09 0.26857 1 0.6043
##
## Backward reduced fixed-effect table:
## Eliminated Df Sum of Sq RSS AIC F value Pr(>F)
## avg_ta_mmol 1 1 0.0045255 0.052141 -263.71 3.6116 0.06499 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## le_t1t3e ~ 1no genotype effect
Simplest linear model:
##
## Call:
## lm(formula = le_t1t3e ~ avg_ta_mmol, data = grdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08480 -0.02009 -0.00231 0.02505 0.07304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.008974 0.025834 0.347 0.730
## avg_ta_mmol 0.012879 0.006777 1.900 0.065 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared: 0.08679, Adjusted R-squared: 0.06276
## F-statistic: 3.612 on 1 and 38 DF, p-value: 0.06499## Analysis of Variance Table
##
## Response: le_t1t3e
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.004525 0.0045255 3.6116 0.06499 .
## Residuals 38 0.047616 0.0012530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = le_t1t3e ~ avg_ta_mmol, data = grdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08480 -0.02009 -0.00231 0.02505 0.07304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.008974 0.025834 0.347 0.730
## avg_ta_mmol 0.012879 0.006777 1.900 0.065 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared: 0.08679, Adjusted R-squared: 0.06276
## F-statistic: 3.612 on 1 and 38 DF, p-value: 0.06499## [1] "R-Squared: 0.0867925136177123"## [1] "slope: 0.0128792737554567"## [1] "p = 0.0649852461152717"## [1] "intercept: 0.00897422202248515"4 Figures
4.1 Assign Colors/Generate Treat Names/Label groups
4.2 Figure 1 (Treatment TA)
4.2.1 TA~treatment (Boxplot)
4.2.2 TA~time (scatter)
4.2.3 Final Figure 1 (TA_Summary_Plot)
4.3 Figure 2 (Calcification~TA)
4.3.1 stats
##
## Call:
## lm(formula = gr_t1t3 ~ treatment, data = grdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23435 -0.12537 -0.02452 0.12646 0.47582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36790 0.05408 6.803 5.96e-08 ***
## treatmentELEV 0.34706 0.07648 4.538 6.11e-05 ***
## treatmentHI 0.36982 0.07648 4.836 2.48e-05 ***
## treatmentXHI 0.45844 0.07648 5.994 7.07e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.171 on 36 degrees of freedom
## Multiple R-squared: 0.5369, Adjusted R-squared: 0.4983
## F-statistic: 13.91 on 3 and 36 DF, p-value: 3.516e-06## treatment emmean SE df lower.CL upper.CL .group
## AMB 0.368 0.0541 36 0.258 0.478 a
## ELEV 0.715 0.0541 36 0.605 0.825 b
## HI 0.738 0.0541 36 0.628 0.847 b
## XHI 0.826 0.0541 36 0.717 0.936 b
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 4 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.## contrast estimate SE df t.ratio p.value
## AMB - ELEV -0.3471 0.0765 36 -4.538 0.0003
## AMB - HI -0.3698 0.0765 36 -4.836 0.0001
## AMB - XHI -0.4584 0.0765 36 -5.994 <.0001
## ELEV - HI -0.0228 0.0765 36 -0.298 0.9907
## ELEV - XHI -0.1114 0.0765 36 -1.456 0.4736
## HI - XHI -0.0886 0.0765 36 -1.159 0.6562
##
## P value adjustment: tukey method for comparing a family of 4 estimates## # A tibble: 4 × 3
## treatment max_value label
## <fct> <dbl> <chr>
## 1 AMB 0.649 a
## 2 ELEV 0.870 b
## 3 HI 0.87 b
## 4 XHI 1.09 b4.3.2 calcification~treatment (boxplot)
4.3.3 calcification~TA (regression)
## # A tibble: 4 × 3
## treatment mean_value sem
## <fct> <dbl> <dbl>
## 1 AMB 0.368 0.0519
## 2 ELEV 0.715 0.0337
## 3 HI 0.738 0.0646
## 4 XHI 0.826 0.0608## [1] 124.6089
4.3.4 Final figure 2 (Calcification_Summary_Plot)
4.4 Figure 3 (linear extension~TA)
4.4.1 stats
##
## Call:
## lm(formula = le_t1t3e ~ avg_ta, data = growth_2000)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08480 -0.02009 -0.00231 0.02505 0.07304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.974e-03 2.583e-02 0.347 0.730
## avg_ta 1.288e-05 6.777e-06 1.900 0.065 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared: 0.08679, Adjusted R-squared: 0.06276
## F-statistic: 3.612 on 1 and 38 DF, p-value: 0.06499##
## Call:
## lm(formula = le_t1t3e ~ treatment, data = growth_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.086972 -0.020711 -0.004481 0.026055 0.073910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03658 0.01132 3.230 0.00264 **
## treatmentELEV 0.02434 0.01601 1.520 0.13723
## treatmentHI 0.02423 0.01601 1.513 0.13895
## treatmentXHI 0.03272 0.01601 2.043 0.04839 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03581 on 36 degrees of freedom
## Multiple R-squared: 0.1147, Adjusted R-squared: 0.04095
## F-statistic: 1.555 on 3 and 36 DF, p-value: 0.2171## treatment emmean SE df lower.CL upper.CL .group
## AMB 0.0366 0.0113 36 0.0136 0.0595 a
## HI 0.0608 0.0113 36 0.0378 0.0838 a
## ELEV 0.0609 0.0113 36 0.0380 0.0839 a
## XHI 0.0693 0.0113 36 0.0463 0.0923 a
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 4 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.Calculate percent increase between controls and highest treatment
## # A tibble: 4 × 3
## treatment mean_value sem
## <fct> <dbl> <dbl>
## 1 AMB 0.0366 0.00881
## 2 ELEV 0.0609 0.0118
## 3 HI 0.0608 0.00949
## 4 XHI 0.0693 0.0144## [1] 89.454484.4.2 LE~treatment (boxplot)
4.4.3 LE~TA (regression)
4.4.4 Final figure 3
4.5 Growth across timepoints – exploration
growth across timepoints.
4.5.1 growth~first half (regression)
growth (calcificaiton and linear extension) from timepoint 1 to timepoint 2
4.5.1.1 stats for t1-t2
##
## Call:
## lm(formula = le_t1t2e ~ avg_ta_mmol, data = growth_t1t2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10099 -0.04062 -0.00082 0.03377 0.12879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02205 0.04053 0.544 0.5897
## avg_ta_mmol 0.02809 0.01071 2.623 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0562 on 37 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1568, Adjusted R-squared: 0.134
## F-statistic: 6.881 on 1 and 37 DF, p-value: 0.01258## Analysis of Variance Table
##
## Response: le_t1t2e
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.021737 0.0217367 6.8815 0.01258 *
## Residuals 37 0.116873 0.0031587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = le_t1t2e ~ avg_ta_mmol, data = growth_t1t2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10099 -0.04062 -0.00082 0.03377 0.12879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02205 0.04053 0.544 0.5897
## avg_ta_mmol 0.02809 0.01071 2.623 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0562 on 37 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1568, Adjusted R-squared: 0.134
## F-statistic: 6.881 on 1 and 37 DF, p-value: 0.01258##
## Call:
## lm(formula = gr_t1t2 ~ avg_ta_mmol, data = growth_t1t2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52003 -0.14493 -0.00234 0.14115 0.52697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11809 0.16899 0.699 0.489066
## avg_ta_mmol 0.18638 0.04465 4.174 0.000174 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2343 on 37 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3202, Adjusted R-squared: 0.3018
## F-statistic: 17.42 on 1 and 37 DF, p-value: 0.000174## Analysis of Variance Table
##
## Response: gr_t1t2
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.9568 0.95680 17.425 0.000174 ***
## Residuals 37 2.0317 0.05491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Percenet increase in LE from t1 to t2
## # A tibble: 4 × 3
## treatment mean_value sem
## <fct> <dbl> <dbl>
## 1 AMB 0.0784 0.0136
## 2 ELEV 0.134 0.0190
## 3 HI 0.135 0.0164
## 4 XHI 0.155 0.0190## [1] 97.633544.5.1.2 regression plot
4.5.2 growth second half (regression)
4.5.2.1 stats
##
## Call:
## lm(formula = le_t2t3e ~ avg_ta_mmol, data = growth_t2t3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30615 -0.02895 0.02564 0.05379 0.11747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01345 0.07259 0.185 0.854
## avg_ta_mmol -0.01844 0.01949 -0.946 0.352
##
## Residual standard error: 0.0932 on 28 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.03099, Adjusted R-squared: -0.003618
## F-statistic: 0.8955 on 1 and 28 DF, p-value: 0.3521## Analysis of Variance Table
##
## Response: le_t2t3e
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.007779 0.0077789 0.8955 0.3521
## Residuals 28 0.243237 0.0086870##
## Call:
## lm(formula = le_t2t3e ~ avg_ta_mmol, data = growth_t2t3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30615 -0.02895 0.02564 0.05379 0.11747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01345 0.07259 0.185 0.854
## avg_ta_mmol -0.01844 0.01949 -0.946 0.352
##
## Residual standard error: 0.0932 on 28 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.03099, Adjusted R-squared: -0.003618
## F-statistic: 0.8955 on 1 and 28 DF, p-value: 0.3521##
## Call:
## lm(formula = gr_t2t3 ~ avg_ta_mmol, data = growth_t2t3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6572 -0.1016 -0.0188 0.1690 0.4270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.23906 0.17527 -1.364 0.183441
## avg_ta_mmol 0.18002 0.04706 3.826 0.000669 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.225 on 28 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3433, Adjusted R-squared: 0.3198
## F-statistic: 14.64 on 1 and 28 DF, p-value: 0.0006692## Analysis of Variance Table
##
## Response: gr_t2t3
## Df Sum Sq Mean Sq F value Pr(>F)
## avg_ta_mmol 1 0.7412 0.74120 14.636 0.0006692 ***
## Residuals 28 1.4180 0.05064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.5.2.2 regression
4.6 Figure 4 (growth across timepoints)
4.7 Supplementary Figure 1 (Property-Property Plot)
4.7.1 Data from your T1
Ideal and other modeled data assumes pCO2 of 400 and the +Alk targets with the 1.26:1, 1:0, and 0:1 to compare methods with keeping Alk targets and letting DIC and pCO2 vary. +0 (2400), Ideal: 2068.82 Na2CO3 Only: 2068.82 NaHCO3 Only: 2068.82
+15 (3900), Ideal: 3243.677 Na2CO3 Only: 2818.82 NaHCO3 Only: 3568.82
+30 (5400), Ideal: 4369.416 Na2CO3 Only: 3568.82 NaHCO3 Only: 5068.82
+45 (6900) Ideal: 5459.844 Na2CO3 Only: 4318.82 NaHCO3 Only: 6568.82
## treatment type sal pH
## Length:16 Length:16 Min. :34.24 Min. :7.710
## Class :character Class :character 1st Qu.:34.39 1st Qu.:8.059
## Mode :character Mode :character Median :34.39 Median :8.260
## Mean :34.39 Mean :8.249
## 3rd Qu.:34.39 3rd Qu.:8.400
## Max. :34.60 Max. :8.968
## pCO2 DIC HCO3 CO3
## Min. : 56.69 Min. :2069 Min. :1819 Min. : 239.0
## 1st Qu.: 313.32 1st Qu.:2660 1st Qu.:1949 1st Qu.: 280.1
## Median : 400.00 Median :3569 Median :2597 Median : 540.3
## Mean : 630.12 Mean :3609 Mean :2886 Mean : 705.7
## 3rd Qu.: 400.66 3rd Qu.:4331 3rd Qu.:3311 3rd Qu.: 859.1
## Max. :3011.36 Max. :6569 Max. :6127 Max. :2227.0
## ALK OmegaAragonite
## Min. :2400 Min. : 3.846
## 1st Qu.:3444 1st Qu.: 4.506
## Median :4220 Median : 8.698
## Mean :4452 Mean :11.355
## 3rd Qu.:5400 3rd Qu.:13.825
## Max. :6900 Max. :35.8354.7.2 create a range of carb chems surrounding your data
4.7.3 Plot the range, overlay your data
Calculate percent increase in growth between controls and different treatments
## # A tibble: 4 × 3
## treatment mean_value sem
## <fct> <dbl> <dbl>
## 1 AMB 0.0784 0.0136
## 2 ELEV 0.134 0.0190
## 3 HI 0.135 0.0164
## 4 XHI 0.155 0.0190## [1] 97.63354## # A tibble: 4 × 3
## treatment mean_value sem
## <fct> <dbl> <dbl>
## 1 AMB 0.512 0.0569
## 2 ELEV 0.880 0.0494
## 3 HI 0.887 0.0821
## 4 XHI 0.991 0.0924## [1] 93.288425 Call Final Figures